ScholarWorks@경상국립대학교

초록

Various techniques for bias correction and statistical downscaling have been developed to overcome the limitations related to the spatial and temporalresolution and error of climate change scenario data required in various applied research fields including agriculture and water resources. In this study,the characteristics of three different statistical dowscaling methods (i.e., SQM, SDQDM, and BCSA) provided by AIMS were summarized, and climatechange scenarios produced by applying each method were comparatively evaluated. In order to compare the average rainfall characteristics of the pastperiod, an index representing the average rainfall characteristics was used, and the reproducibility of extreme weather conditions was evaluated throughthe abnormal climate-related index. The reproducibility comparison of spatial distribution and variability was compared through variogram and patternidentification of spatial distribution using the average value of the index of the past period. For temporal reproducibility comparison, the raw data andeach detailing technique were compared using the transition probability. The results of the study are presented by quantitatively evaluating the strengthsand weaknesses of each method. Through comparison of statistical techniques, we expect that the strengths and weaknesses of each detailing techniquecan be represented, and the most appropriate statistical detailing technique can be advised for the relevant research.

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